A researcher wants to study the factors affecting a person's decision to buy a car. For his study, he selects a random sample of 100 people from a city and estimatesthe following regression equation:C = - 7.35 + 0.44/+ 0.36M – 0.27P,where C is a binary dependent variable which denotes the decision to buy the car (C equals 1 if the person decides to buy the car, and 0 otherwise), / denotes themonthly income of the person (I equals 1 if the income exceeds $5,000 and 0 otherwise), M denotes the car's mileage (measured in miles per gallon) and P denotesthe price of the car (in thousand dollars).The researcher wants to test the hypothesis that the coefficient on /, B4, and the coefficient on M, B2, are jointly zero, against the hypothesis that at least one of thesecoefficients is non-zero. The test statistics for testing the null hypotheses B1 =0 and B = 0 are calculated to be 1.55 and 1.25, respectively. Suppose that these teststatistics are uncorrelated.The F-statistic associated with the above test will be(Round your answer to two decimal places.)

Answered: Image /qna-images/answer/aaa5974b-39c8-4837-aac7-5bc98a48760b.jpg

Answered: A researcher wants to study the factors…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The scatterplot below shows Olympic gold medal performances in the long jump from 1900 to 1988. The long jump is measured in meters. Use the regression line to predict the average predicted long jump distance in 1950. A. The average predicted long jump distance in 1950 is 7.94 meters. B. The average predicted long jump distance in 1950 is 362.7 meters. C. The average predicted long jump distance in 1950 is 3,054 meters. D. The average predicted long jump distance in 1950 is 6.893 meters.
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The regression equation is: ŷ = 67.16 + 8.417x where ŷ is the miles traveled, and x is the MPG. The sample size used was all 110 MPG records. The correlation coefficient r = 0.620. Use the information to obtain an estimate of my mileage if my MPG is 22. Is it option: a.) cannot estimate ŷ rcrit = 0.195; the correlation IS NOT significant b.) ŷ = 252.33 rcrit = 0.195; the correlation IS significant c.) ŷ = 252.33 rcrit = 0.187; the correlation IS significant d.) cannot estimate ŷ rcrit = 0.187; the correlation IS NOT significant
The table shows the number of goals allowed and the total points earned (2 points for a win, and 1 point for an overtime or shootout loss) by 14 ice hockey teams over the course of a season. The equation of the regression line is y=−0.532x+211.813. Use the data to answer the following questions. (a) Find the coefficient of determination, r2, and interpret the result. (b) Find the standard error of the estimate, se, and interpret the result Goals Allowed, x Points, y215 112210 104217 103219 96259 85267 77281 51201 102214 99206 103216 94200 89263 70243 72
Data from 147 colleges from 1995 to 2005 (Lee,2008) were tested to predict the endowments (in billions) to a college from the average SAT score of students attending the college. The resulting regression equation was Y = -20.46 + 4.06 (X). This regression indicates that: a. for every one-point increase in SAT scores, a college can expect 4.06 billion more in endowments. b. most colleges have very high endowments. c. for every one-point increase in SAT scores, a college can expect 20.46 billion fewer in endowments. d. for every one-dollar increase in endowments, the college can expect a half-point increase in SAT scores.
A popular board game manufacturer was interested in the relationship between the amount of time it takes to play a game and how well that game is rated among board game players. Information was collected on several board games and was used to obtain the regression equation ŷ = 27.273x + 18.182 where x represents the time it takes to play (in hours) and ŷ is the predicted rating of that game (in points). What is the predicted rating of a game that takes 1 hour to play? –0.63 points 9.091 points 45.455 points 1,654.562 points
The City of Bellmore’s police chief believes that maintenance costs on high-mileage police vehicles are much higher than those costs for low-mileage vehicles. If high-mileage vehicles are costing too much, it may be more economical to purchase more vehicles. An analyst in the department regresses yearly maintenance costs (Y) for a sample of 200 police vehicles on each vehicle’s total mileage for the year (X). The regression equation finds: Y = $50 + .030X with a r2 of .90 What is the IV? What is the DV? If the mileage increases by one mile, what is the predicted increase in maintenance costs? If a vehicle’s mileage for the year is 50,000, what is its predicted maintenance costs? What does an r2 of .90 tell us? Is this a strong or weak correlation? How can you tell?
A social scientist would like to analyze the relationship between educational attainment (in years of higher education) and annual salary (in $1,000s). He collects data on 20 individuals. A portion of the data is as follows: Salary 43 49 1 35 Education 7 7 Click here for the Excel Data File a. Find the sample regression equation for the model: Salary Be + B1Education + e. (Round answers to 2 decimal places.) Salary = 37.21 + 6.68 b. Interpret the coefficient for Education. Education As Education increases by 1 year, an individual's annual salary is predicted to increase by $8,590. As Education increases by 1 year, an individual's annual salary is predicted to decrease by $6,680. As Education increases by 1 year, an individual's annual salary is predicted to decrease by $8,590. As Education increases by 1 year, an individual's annual salary is predicted to increase by $6,680. c. What is the predicted salary for an individual who completed 7 years of higher education? (Round coefficient…
A sports statistician was interested in the relationship between game attendance (in thousands) and the number of wins for baseball teams. Information was collected on several teams and was used to obtain the regression equation ŷ = 4.9x + 15.2, where x represents the attendance (in thousands) and ŷ is the predicted number of wins. What is the predicted number of wins for a team that has an attendance of 17,000? 83.3 wins 98.5 wins 258.4 wins 263.3 wins
The table shows the number of goals allowed and the total points earned (2 points for a win, and 1 point for an overtime or shootout loss) by 14 ice hockey teams over the course of a season. The equation of the regression line is y= - 0.558x + 216.186. Use the data to answer the following questions. (a) Find the coefficient of determination, r, and interpret the result. (b) Find the standard error of the estimate, s,, and interpret the result. Goals Allowed, x Points, y 218 212 216 220 257 266 274 200 211 206 216 204 264 244 O 111 106 99 90 86 83 45 105 100 101 94 83 67 68 (a) ? =O (Round to three decimal places as needed.)
The police chief believes that maintenance costs on high-mileage police vehicles are much higher than those costs for low-mileage vehicles. If high-mileage vehicles are costing too much, it may be more economical to purchase more vehicles. An analyst in the department regresses yearly maintenance costs (Y) for a sample of 200 police vehicles on each vehicle’s total mileage for the year (X). The regression equation finds: Y = $50 + .030X with a r2 of .90 If a vehicle’s mileage for the year is 50,000, what is its predicted maintenance costs? What does an r2 of .90 tell us? Is this a strong or weak correlation? How can you tell?

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